Episode Transcript
[00:00:00] Speaker A: Your LTV is 100 and you know, LTV is not just a number. Your LTV is not $100. It's crazy reading the news in 2026. There's this narrative that AI is going to replace human jobs, that you can just vibe code solutions into existence until of course they do it. They see that these AI tools hallucinate, they make things up out of they're very unpredictable. If me and you were to build a company, we would immediately have all our data and then at a certain point it becomes unsustainable to download all
[00:00:32] Speaker B: this data and Jigsaw welcome to the Entrepreneurs Logbook podcast. I'm your host Zach Bernard. You can find me on Social at its zakb. In each episodes I bring on experts from various industries for to learn about their strategies and insights driving extreme business growth. Today we're joined by Team Shay, founder and CEO of Latticework Insights, a data science and analytical firm that helps retail and DTC brands turn fragmented data pieces into real algorithm actionable business insight. He spent the last 25 plus years at the intersection of data science, software engineering and marketing at stints at powerhouse's agencies like Razorfish. He's also a 3X startup founder who's raised over a million dollars. And over the last seven years at his company he's worked with over 70 brands including the NBA, UFC, Reddit, Facebook, LinkedIn, Pepsi, Nissan, pretty much brands that you've all ever heard of. He's also been featured in the Ad Week, Wall Street Journal and YO Finance. So Tim's been around the block to put it very simply here. So Tim, welcome aboard. Thank you for taking the time to join us here.
[00:01:33] Speaker A: Thanks so much for for having me on the pod.
[00:01:36] Speaker B: I love it. So you've been around the block, Tim. So I'm really excited to have this conversation on like the data science science, data science, technology, marketing side of things. So a lot of like different things that you've been obviously an expert on here, but as this is like an entrepreneurship business podcast, the one main thing I always like to ask anyone that comes on the show is if you have to restart your company from ground up from zero, the one that you're running right now because then you ran a couple of companies, is there something that you would do differently that you feel a lot of entrepreneurs get totally wrong?
[00:02:07] Speaker A: I mean it's a great question. I started the company back in 2018. Just prior to running the company I was working for someone else. We're selling data products into big media buying agencies. Okay. It was crazy. Back in 2018, I was getting told, no right, like, left and right. And I actually had one prospect pull me aside. He goes, bro. He goes, sam, listen. He goes, we love your data platform. It seems pretty cool, but we've actually got 20 other data platforms in house that do slightly different versions of what yours does. So we're. We're going to pass, man. Like, we don't even have time to log into these other platforms, never mind create insights for them. And I remember having this moment of being like, oh, I know we're a SaaS company, but what we'll do is we'll provide you guys a managed service. We'll do some consulting work for you, we'll do the downloading for you. And I remember, you know, like, you know, security is like, dragging you out of the building. You know, at that point, it was like, no, no, we'll help you build this lattice work, and we'll help you, you know, kind of get to the insight.
And, you know, they throw you onto the sidewalk and, you know, these. These, you know, giant garbage bags in New York City. And I remember, like, dusting myself off and having this epiphany looking around at all these companies. 100% of companies all have this same problem where they've got their core data stuck in all these different SaaS platforms. Right? Zach, if me and you were to build a company like today, we would immediately have all our data in, like, Facebook. We'd be marketing on TikTok. We'd have some influencers, and that's just our CAC.
And we've got, like, Shopify and Amazon and Sephora.
That's our LTV component. We've got Klaviyo and Netsuite and the supply chain. And at a certain point, it becomes unsustainable for me and you to download all this data and jigsaw it together and try and come up with what our LTV CAC ratio is or what our return and retention and margin numbers are. And so I'm like, damn, dude. Like, companies need to build this lattice work. They need to join all the data together, and then they need someone to help them get to the inside. We've got 25 years of domain expertise in retail and D2C, e.com and marketing and advertising, and companies need this sort of intelligence layer across all of these disciplines. People that are multidisciplinary thinkers to help them break out of their current growth patterns or really kind of experience that unlock the growth that they're headed for.
So, yeah, that was Tiffany in 2018. And I'll tell you, even in 2026, in this era of AI and so much has changed in seven, eight years, it's the same exact issue.
And if I had changed anything, I would wish I could just double down, triple down, quadruple down on this idea of like consultants, like super smart human beings are the ones that are going to solve all these problems between all these little SaaS, platforms that don't really turn talk to each other.
So yeah, I would just really like quadruple down on humans.
[00:05:09] Speaker B: That's interesting because everyone I speak to, it's like a different perspective. It's like, hey, you need to double down, like AI, like, why are you not using it enough? But you're like opposite of that. Like, I know you obviously do use AI, but you're like, hey, we should be doubling down on people who have that expertise, who are able to support us, who know how to like build the entire infrastructure and like how to map it out efficiently. And you're like, so right, because there's so many different platforms that we're using and with like all this data scattered around, like, you never have like accurate like numbers, data and everything. And that literally affects your business because you're taking decisions based on numbers that are not even accurate. So being able to have like the correct data analytics, I mean, it's so important. I feel like a lot of people totally like disregard that. And that's like somewhat where you guys come in here. And that's good.
[00:05:59] Speaker A: No, no, I mean, it is true. And I find that there's sort of two cohorts of companies. There's companies that look at data and analytics as a sort of call center. And then there's these elite brands that see the power of data and analytics that maybe they struggle with implementing it, but they know that there's some sort of unlock that they're going to experience. And if sort of, if done correctly, if you can unlock the power of. Yeah, you mentioned fragmented data. It's like this giant jigsaw puzzle where a little bit of our data is stored in one place, a little bit of it is stored in another place. Some of it overlaps and conflicts where the numbers are slightly different. But even if you just use one or two pieces, there's these giant holes in this jigsaw puzzle. And without the expertise and without the actual data in one place, it's really, really hard for companies to unlock their growth.
[00:06:48] Speaker B: You need to have the puzzle builder that can put the puzzle pieces at the right place. Here and going on to lattice work because I'd love to hear for the people that first time, what does this company do? I love to understand what's the process when a big brand, I mean you work with some pretty big brands, like I mentioned a couple earlier, and I'd love to understand what kind of that process looks like and typically what's usually the problem that they come to you with and what does that process look like from getting them to actually like a real solution that actually works like intentionally together?
[00:07:20] Speaker A: Yeah, yeah, usually. Honestly the folks that come to us, let's say like smaller brands, folks that are about to raise like a series A, series B round like they, they've created this rocket ship, they're doing really good and what they find is that like a really important person in their company, it's usually like the Chief Operating officer, the Chief Financial Officer, CMO, sometimes they're spending like four, eight hours a week, like all day Monday, downloading 20 spreadsheets from 20 different platforms and jigsawing data together to create this report.
And so it's this huge time drain, this huge brain drain on the company with this super critical resource is being totally eaten up. And then they get the report together and God forbid someone asks them a follow up question, they say, well, we think our LTV cap ratio is X. And someone says, well how is that trending over month over month?
We're a big vc, we're about to write you a big check. What if we take out those sorry folks from Black Friday, we gave them a 15% discount to get them in the door. What if we've removed them from the cohort? What does the data look like then?
And the whole company has to scramble to put together this brand new report.
Meanwhile, the VCs are sort of like, you know, tapping their thumbs and trying to figure out, hey, does this company have what it takes to take on additional capital to, you know, to really launch this company?
And many companies fail at this stage. And this is oftentimes when we encounter them as they'll bring us in, they say, you know what we, it's time for us to grow up. It's time for us to build this data pipeline. We need a different lens on the business, a different point of view on the business. So we'll come in and we'll build a data pipeline. We'll build a data warehouse for them. We'll model all those important metrics for them, ltv, CAC retention margins and so forth.
We'll get it all in a Dashboard that's interactive, that they can ask follow up questions for and then we'll layer on what we call an intelligence layer. We'll help them upgrade their data culture so that they can speak data, they're fluent in data, they know when to go to the data team, they know when they can answer questions themselves and really help them with things like AB testing and growth strategy and vision and so forth. And the companies that invest in this process, they see this inflection point in their growth. This whole set of things are just unlocked for them.
[00:09:50] Speaker B: Yeah, and I feel like a lot of companies, they feel that they have like the data coverage, they're like, hey, like we have a couple spreadsheets, things are organized. Maybe they have a dashboard running or maybe they have someone on like the, the opposite of things as you mentioned, like every Monday. Like they're just scrambling to get things together. But like from like your perspective, like where you sit, what's kind of going like under the hood that makes like that this false sense of like security and like what does it actually cost them for people to realize? Because I feel a lot of the companies, they only realize that a couple years later down the road when there's been this big, big like return on investment that they've been missing or like efficiency in like their entire operations. And I feel that's, that's a big hurdle for a lot of people that they don't even know they have.
[00:10:36] Speaker A: Yeah, exactly. I think that there's this, there is this false sense of security. There's so many SaaS platforms out there. It's hard to go to someone's website and understand quickly what this SaaS platform does or does not do. They make outrageous claims as to what they.
There's many platforms, let's say Triple Whale is a good example. Triple Whale is a great company when it comes to sort of like marketing attribution.
They'll flash LTV on screen. Your LTV is 100.
And you know, LTV is not just a number, it's a sort of story.
So, you know, we'll look at that number and we'll say, okay, well we can pretty confidently say your LTV is not a hundred dollars without knowing anything about your company. Because that number includes people who you required in the last two weeks.
It includes people that you gave the 50% discount to on Black Friday. And it doesn't tell a story about how long it takes you to get from purchase one to purchase two. It doesn't tell a story about whether you were profitable on the first purchase. Many companies go into debt on the first purchase, hoping that the second purchase will pay down that debt. So that CAC payout period is super, super important. And so we'll take those LTV numbers and we'll unpack it and we'll show them the story of the customer journey, customer behavior.
And they'll say, actually, the predicted LTV for a customer like this, you get a small, medium, and large type of customer. And the predicted LTV of this customer is actually $200. You've only got them at 100. So there's $100 of headroom in there you could get them to. And it's just waiting to be unlocked. And so I think when folks see that for themselves, then they say, oh, now I see the power of, you know, data and analytics. There is a. We're talking about roas and advertising, the sort of return on advertising spend. We think if there's a return on analytics spend, people should really be thinking about, you know, there should be a 2, 3, 4, 5, 10x return on the investments that they make in analytics. And we need to show the ocean to see how that works.
[00:12:38] Speaker B: I mean, just with that example that you gave, like, you said, like a platform.
What's the name again? I forgot. Is it Whale?
[00:12:46] Speaker A: Triple Whale. Great, folks, really good at marketing distribution. But, you know, I think many of these platforms distill these metrics down to just a number on screen. That's just like. This is a very misleading metric, guys.
[00:12:57] Speaker B: Oh, yeah, I've seen it like, on like X, Twitter, whatever you want to call. Like, I've seen it multiple times. I just forgot the. The exact name. But it's a good example, like you mentioned, where maybe you're gonna have someone's gonna show like a hundred dollar, like ltv, and then you dive a little bit deeper and then you realize it's 200. So if you had just stayed with that perception, it's a hundred. Maybe you're gonna have certain marketing efforts to make sure you get to that hundred. And then you. But you could actually get 200 if you did it properly. You knew your analytics. So you talk about, like, roas in the terms of not ad spend, boss, analytics spend. That's like 2x LTV. If you actually knew that's the LTV that you had. And a lot of people just don't know about it. So it's more so about, I mean, the right platforms, but also having like a good combination of them, like having a system that interwinds, like every single platform. They use in an efficient manner. Because I believe Triple Whale does take a couple platform that you have, but it's not next level or a system specifically tailored for a specific company because everyone obviously has different needs. There's probably a little bit of correlation and similarities, but I think that's a big part missing from what you were saying here.
[00:14:05] Speaker A: Yeah, I think a big objection we often get is people say, oh, we have an analyst in house, we got the super smart kid, he majored in economics, wiz and Excel and they said, gosh, you know, let us work Insights. You guys are been in the game for a long time. You guys are really expensive.
And so usually we'll unpack like a couple case studies and we're like, listen, this company invested a lot of money into building this stack. So just not just one person sitting in Excel, they've got this modern data stack now and they've got, they've upgraded their data culture. So now everyone across the organization feels empowered to speak Data to everyone in the organization is moving towards one true north metric. Rallying people around this idea that like, you know, can we all agree that like today is Wednesday, you know, everyone says okay, great, today is Wednesday. Like can we all agree we're trying to like move LTV up, we're trying to shorten the time to second purchase. Okay, great, can we all agree that we're going to a B test just towards that one metric, that we're going to move the company towards shortening the time between two purchases.
So it says, great, now we can do the hard work of actually moving the company forward.
And those are the big unlocks that people see where they're like, hey, you know what? The 10, 20, 30, $50,000 that we spent on lattice work Insights has made us $250,000.
We didn't see all this churn in the same way before we fixed this, our churn problem. We didn't see how much more LTV we could be capturing. This is, this is a huge unlock for us. So yeah, so it's a very common objection that we get, but something we try and unpack pretty quickly.
[00:15:41] Speaker B: Yeah. And I feel it's about creating a data driven culture as well. Everyone again, as you mentioned, they're all working to true north where they have one objective, one KPI they're trying to meet. And having more accurate data obviously makes that a bit easier here.
I know we talked a little bit about AI earlier and I don't want to get back to that because with data, I mean, I'm sure you've seen that there's so many new platform you can use, like AI within like your spreadsheets, like Excel makes everything. I mean like investment bankers, like you'll see on like X or Twitter sometimes like, oh, investment bankers are not going to have a job anymore, like analysts and stuff like that with AI. But there's this narrative that's going to make like everything easier that some brands will be able to just plug it in. It will handle most like the heavy lifting. But what does that actually look like on the ground when the company tries to implement that? Like an AI driven solution without the right foundation in place? Kind of curious to see like what happens in practice because I have a feeling it just becomes a mess if you don't do it correctly.
[00:16:39] Speaker A: Yeah, I mean you still answer. I mean it's very difficult. I think that there's this.
It's crazy reading the news in 2026 and it's crazy being on social media, Twitter and LinkedIn. There's this narrative that there's going to be massive unemployment and AI is going to replace human jobs that you can just Vibe code solutions into existence.
And it's wild because we've all interacted with these platforms. I think maybe everyone on the planet has had that existential panic. They've stared into the void and they were like, wow, maybe some of this is true.
Until of course they do it. Until of course try to push something into production and they see that these AI tools hallucinate, they make things up out of. They're very unpredictable.
There is a whole different lattice work that needs to be built in order to support agentic applications.
There are agentic workflows, there is memory, there is the models themselves. There is this idea now of computer use and browser use, of it taking over Windows and doing things on your behalf.
There's guardrails that you need to put in place that it doesn't just go rogue and you know, mass email a bunch of people the wrong type of message.
And so we've gotten, I would say my platform or my pipeline has sort of doubled in the last two years where one part of my platform is my traditional business and the other part of my pipeline are people showing up at my doorstep with Vibe coded applications that are almost there, they're almost ready to go into production. And we look, has anyone read this code? Have you guys looked over what's actually been built by these AI agents? Yeah, yeah. Well, I don't know. We got lost in the sauce. Right. We were looking at LangChain and we Langsmith and we had this pinecone database and we had this. We kept upgrading the Claude models along the way and it's a big fat mess. We think we're about 80% done. We're like, you should probably start by throwing it all in the trash and starting over.
And so I think that this is true across any discipline is that it's really, really, really good at finance if you're not good at finance.
It's really, really good at medicine if you're not a doctor. Right. People get really excited about a build software, but they're not software developers. And so there's this weird phenomenon that the AIs really do need humans in the middle. They really need humans sort of working the keyboard and kind of ushering it step by step by step.
It's really difficult and frustrating place for folks to be because again there is this narrative whether you're reading the Wall Street Journal or whether you're listening to some TikTok influencer being like, AI is here. The agentic future has arrived. Everyone's making 100 grand a year spinning open cloth servers and sending mass email campaigns and just they're printing money.
And the reality is, is those people are selling coaching sessions, they're not printing money making, you know, sending mass emails. And so there is this harsh reality that a lot of companies run into.
They realize that building agentic applications is really hard and really unpredictable and you need really good software developers and finance people and operations people to actually keep them on track.
[00:19:55] Speaker B: Yeah, I mean I've seen so many example people like vibe coding stuff and being like your total mess. And I mean we can take it from you. See, I mean I know you run like your own like event for specifically gentech, like AI and like la, you guys at San Francisco too, if I'm not mistaken. And yeah, like New York coming up in May.
So I know you've obviously been in like software engineering too. So you haven't started vibe coding like yesterday some application would clot and then call it a day. But yeah, I mean I literally had a training with our team this morning. It was like. So one of the things you have to keep in mind is AI is often wrong. Double check it always in my experience, it's never right like there, there's always, it's right in some capacity but there's always going to be like some quality assurance that you have to run in the back end. And I feel with like software it's more than just quality assurance. Like you have to go through the entire thing to make sure that everything looks good here. And yeah, I mean, I think it's, it's something that a lot of companies are going to learn. You can't just try to do it yourself. Sometimes you will have to work with consultant and experts. I mean, I feel that's why yourselves, why, if I'm not an expert in something, I'm going to go hire someone that actually knows what they're talking about because they can help us with that. And I feel that's obviously very important here. But talking about like investment and everything, I'd love to go back to the, the roas aspect of things because you mentioned earlier. And I mean I, I can see like how we can be perceived, but a lot of founders, they're going to treat the, the analytics more as like a, a cost of doing business rather than something that you're like, investing that's going to actually produce like an actual return. Like marketing, for example. And I love to hear like, how you think about the, the actual ROI of like analytics spend and how should like a brand be measuring whether that investment is working for them? Because obviously anyone that's investing into something, they'll be able to want to see like some data, KPI's numbers, seeing like how it's actually performing. And I feel that's probably one big question that a lot of founders are going to have.
[00:21:46] Speaker A: It is a big question that they have. And honestly, look, you know, being in the game for 25 years, we have a lot of empathy of the other things that brands are struggling with. We have a lens where like, data analytics is everything. We forget that there's people that are giant cardboard boxes are showing up from the, you know, the docks and a human being is opening the cardboard box and taking product out and looking at a paper PO and reading the numbers. We forget that that's really hard, tedious manual work where technology has not yet made an impact on their lives.
We realize that marketing is like an art, finance and operations is an art, and that we can't just come in with a hammer looking for a nail trying to solve everything.
But indeed, there are these major. A lot of people say, Tim, you know, the, the era of big data is over and the era of human intuition has arrived. What about my intuition about the placement of my product in a store or the messaging, the way we speak to our customers? And I say, great, you're totally right, you're totally right. The data has no business chiming in on those things. But just real quick Follow up question, which campaign last year performed better?
I know you have an intuition about which one can perform better, but the one that was wildly successful from the top line, like, was the margin, was the margin smaller because you spent so much on marketing and you gave so many discounts? And those people that you acquired on that tent pole event, you spent so much money on marketing, did they come back right? Because a lot of times on Black Friday, let's say most e commerce companies, the average customer or the average e commerce customer, about 80% of their customers never come back.
Average e Commerce Company, 80% of their customers never come back on Black Friday. It's like 90, it's like 95.
They're bargain hunters. They're very fickle. They maybe would have bought in October or December, but you gave them the discount in November.
So just wanted to gut check with all you smart marketing folks, which one performed better because you may have just given a huge discount to a bunch of unloyal customers who are never going to come back until next year. And meanwhile, this one event that didn't seem so groundbreaking, does people buy all the time. That's your core audience.
And so I think that intuition has a huge part of the puzzle. As data people, we have intuitions as well. But there are these times where we need to look at the sort of cold, hard facts where companies are like, if we could just hit this one margin milestone, if we could just hit this one growth milestone, we could unlock all this venture capital, we could do that big promotion we've been looking to do with Sephora, if we could just do this one thing.
And you're like, well, intuition's only going to get you so far. We need to be able to measure the efficacy of all these things, make sure that people are ab testing and experimenting and being really honest about what's working and not working. And until they have a data and analytical lens on those activities, a lot of that activity can go undiscovered.
[00:25:08] Speaker B: Yeah, and I feel like because you're a data person, intuitively you're already thinking about these types of things. But so many founders, business owners are not thinking about that.
Like, it's so true. Like we track like all of our KPIs, like all your outbound, like for the past like three years, like I have day by day, all of our KPIs. And I feel that's like a very important thing because I can see like, okay, at this point in time, this was performing well. This was not performing well. We did this, this, this I feel not everyone tracks those numbers. They don't track these tests and everything. And when you do, you have a much clearer picture where you're going, where you're trying to go in that true north. As you mentioned before, like your North Star.
[00:25:45] Speaker A: Totally. I think as an entrepreneur, you're very close to your business. You're very passionate about your business.
You know, I remember I had a great conversation with my wife.
I went to Cannes, you know, I went to the Cannes Lion Festival and I was so excited. When I came back, I was all tan and I've been sitting on the beach sipping rose and I networked with all these people.
You got to go there for like seven days. You got to drop like, you know, 10 racks just to get out there and, you know, be part of the whole, the whole charade. And my wife goes, so, so how you, how much money did you make?
How many. So how many, how many business cards did you get? And I was like, babe, you don't understand, like the, the spectacle of Cannes on the beach was just amazing. And she goes, uhhuh. And so how many clients did you get? And you, you need that gut check sometimes as a founder where you're like, oh, right, right, right. So the, the roas was, you know, spent $10,000 and I maybe got nothing out of it. Or maybe this was just a top of funnel exercise where now I have some more brand recognition. But it was not a bottom of funnel exercise where I was, you know, printing money off of this activity. So I, you know, as a founder, I have a lot of empathy with what other founders go through.
[00:26:53] Speaker B: Yeah, that's fair. It's kind of funny that you mentioned that example because so many people are going to be spending on going on trips and everything and then they're like kind of forgot to network with people that could be potential clients here. I just enjoyed the weather too much and the mimosa or whatever you want to call them, the rose. So it's always an important one here. And another thing I'd really be kind of curious to hear is because some brands, they're going to have a technical team. They might have an engineering team, maybe like an engineer, analytics person, or maybe even a technical co founder. They might feel like they got everything like the data piece covered in Naos, but there's kind of like a gap that usually exists and I'd love to understand what's kind of stopping them from even being able to see that gap. And then from there it's already settled down.
[00:27:45] Speaker A: I think you're asking a great question, I think, particularly with bigger companies, right? So I say big companies and small companies, they suffer from the same problem of data being spread out all over the place.
But let's be honest, a lot of these young e commerce companies don't have a technology team, they don't have a data team. They're selling mushroom infused fizzy water or they're selling apparel. And data scientists have no place in that environment.
Bigger companies, maybe they've already built some sort of latticework, they've already built this sprawling data pipeline, they've already built a huge proliferation of reports where many stakeholders are logging in to suit up tableau. And they're asking Claude agents questions.
And for those companies, a lot of times my conversations with them are around leadership, is they say, hey, we have a lot of these great folks in house and they've got five, ten years of experience.
There's a need for us to bring in some of the adults and we need someone who's got like 25 years of experience working with some of the biggest brands in the world, biggest ad agencies on Madison Avenue. And we need to help upskill everybody. We need to think about some of like the old school, you know, mundane blocking and tackling. We need to think about unit tests. We need to think about actually statistically relevant A B tests.
We need to help folks upgrade what, you know, data culture. I think that there's this sentiment at a lot of places is where when you're about to go in to a big meeting with stakeholders or brief Wall street, they say, oh, we built the deck. But before we finish, let's check in with the data team.
Let's check in with, maybe the data team can whip up a quick slide, slide, 19 of 20.
And we're like, well, hold on one second, hold on one second. We should check in with the data team. In the beginning of the process, they should be woven into the DNA of everything that they do. And let's be fair to marketing and operations and finance people.
Data and technology people are nerds.
I'm a nerd. I get it. Many data and technology people have failed to acquire the finesse, the acumen, the vocabulary to be invited back to many of these meetings. They show up with the, you know, they used to have these things called pocket protectors. You know, they show up with the proverbial pocket protector to the meeting and everyone's just like, who brought the nerds to this meeting? And so there's a little bit of upscale skilling of, hey, guys, you guys need to speak the language of, you know, stakeholders of Wall street, of ltb, cac, and don't show up talking about the latest, coolest, nerdy gadget that you guys put into the data pipeline. So I think leadership tends to be a big thing that these bigger companies gravitate towards.
[00:30:26] Speaker B: Yeah, no, that's kind of hilarious because you need to, like. And I mean, yeah, it's so true because, I mean, I remember at someone else in your podcast, probably one of our first guests, and you mentioned, like, yeah, with engineering and like, marketing, for example, there's always like a discount connect. And what we're really, really proud of is we've been able to bridge that disconnect. When you're pushing out products, you want to make sure that both teams are in the known. And I feel that's a bit similar for brands, too. Or the engineering data.
Maybe not engineering, but marketing data. They're somewhat, not too, really close to each other. And that's a big mistake. I mean, the good example you mentioned, they're going to VCs, and at the end of the presentation, then they start, like, building out, like, get the data team in here. And then you realize, okay, probably we should have added them before we started building this entire thing out. And I feel like a lot of people just totally forget about that.
That's like, a big thing. And just for, like, other brands, let's keep it a bit more, like, general. Like, it could be.
Actually, let's start with smaller brands and then we can go a bit to, like, the, the bigger, like, enterprise or so companies. But I'd love to understand for like, any company, they're like, hey, we, we want to get our data in check. We've done a couple of things with the spreadsheet, the dashboard, it's working fine, but we're starting to see, like, a few leaks that are going through, like, the cracks, and we want to optimize, be more efficient. Is there like maybe two, three bullet points? A couple things you say, like, hey, today this is what you should be looking at to be able to get ahead of that, to make sure it doesn't continue leaking further?
[00:31:54] Speaker A: Yeah, I mean, I think for younger brands, if you're at the sort of like, you know, 20, 30, $50 million in revenue range, many of them start to feel that they've hit a plateau. Maybe they're.
Their top line is still increasing, but they feel like they've hit a plateau where they're like, it's really expensive to acquire new customers.
Or they see that churn at the bottom and they see the, hey, gosh, you know, 80% of our customers never show up again. You know, all we're doing is giving, you know, Mark Zuckerberg more and more money to bring more people into the, into the funnel. And 80% of them are leaking out the bottom.
And so usually they hit this frustrating point where either their key person, like one of the smartest people in the company, is stuck in spreadsheet hell.
And that's honestly when people will reach out as like, I can't do it anymore. I can't spend all day Monday, every Monday, building these reports manually. Or they say, man, our ROAS is just stuck.
Our churn and retention rates are just stuck.
And gosh, we really feel like we're so close on the fundraising front. If we could just do X.
We're about to get Erewhon's attention. There's a line wrapped around the corner to get into Whole Foods and Erewhon. Right.
We were so close about making the case to be in their shelves. If we could just do this one thing. And so it always feels like there is a feeling of stuckness where a key operator, coo, cfo, cmo, head of, VP of growth, just feels like, I need an unlock, I need a new lens on the business.
I gotta get my Mondays back.
And that's usually when they reach out to us, when they're, they're trying to achieve some inflection point in their growth, but they just feel like they can't.
[00:33:38] Speaker B: Yeah, I mean, I feel like it's probably a pretty common occurrence where you see that, like, pretty often, like, happening with those companies here. And I mean, like a big advice. I mean, I'm sure you've heard it so many times, but I mean, I would say it comes more from like, marketers, but, like, just spend more money. Just like, put more, more money into your ads and your. You'll get more customers out of it. But that advice, I mean, it's not technically flawed, but in a capacity, you're just going to be putting money in a machine that's leaky without optimizing it first. Ensure there's more efficiency and you're getting more for your buck out of it versus just giving more money to Zuck, which obviously no one likes to do that in the first place here. So that's more for like the, the smaller brands, but for like the, the bigger one. Maybe it's like enterprise or anything like that. I'm assuming these types of brands usually will maybe have a data team, maybe have like an analytics person, engineering or anything like that. And I love to kind of understand, like, what's kind of the difference because some of them do have a team in house, but maybe they're just not doing it efficiently as they should be.
[00:34:37] Speaker A: Yeah, I think a lot of bigger brands too suffer from this kind of FOMO problem where they see lots of maybe younger, more nimble brands doing cool stuff with influencers or activations and they say, gosh, we think we found those the solution to all of our problems. We're going to get Charlie D' Amelio to do a TikTok video for us. So maybe a dated example. We're going to get Addison Rae to come in and she's just going to crush it on the numbers front.
And I think that data people can react to that and say, great, we'll build a dashboard in support of the Addison Rae activation. We're like, okay, guys, what you've just described here is not an AB test. You've set up an A test, you've come up with a new idea and you're just going to try it and see what happens. But there's no statistical rigor to this experiment. And I know that sounds like hair splitting and annoying and nerdy, but the reality is throwing spaghetti at the wall is merely that it's very hard to learn whether Addison Rae caused this big spike in sales or we just had a good month and you really don't know, maybe that Facebook ads were overpriced performing that month. So I think this statistical rigor is a big piece of the puzzle. It feels sort of like annoying and sort of tedious and hair splitting to say it to folks. But I think that there's that, you know, having the expertise to rally a group of people around this common idea, like, we're going to do it, structure the test this way and everyone's going to be open minded. And if a test does not work, it's okay. We've learned something and now next month we're going to do it with a different influencer.
If it does work, let's double down on that.
How much should we double down or triple down? Great question. Let's take a look at the data, let's talk it through. I think that vocabulary and I think getting those marketing and data people to get along so they can speak the same language to each other is a really hard thing to do. But it's something I've been focusing on just forever.
So, yeah, trying to get unicorn generalists or left Brain. Right. Brain thinkers.
And those are the ones that can be really effective in breaking through.
[00:36:43] Speaker B: Yeah. And I mean there's always this disconnect with like the marketing data side of things, which, I mean, like, I would think it's like one in one. I mean if you're marketing, you're gonna have data, you're gonna have to look at it to make decisions based off of that. So you kind of need a data team or someone that actually knows what to look for in the data. But it's interesting that you mentioned like, oh, maybe we're just having a good month, maybe the odds are just performing very well this month or maybe there's this other factor that we don't control that affected our sales. And I mean, I feel that's a common thing. You can't control every single factor. There's always going to be different things that comes into play. But what you can do is control your data, make sure it's organized, look at it and then make decisions based on the data. As you mentioned, you can just throw spaghetti at the wall, see what sticks and then just make other decisions based off of that. I mean it might work, but in my experience it's not going to be a long term solution that you shouldn't be doing it every time. Just throw it at the wall here. But I love that. Anything else that you would add to that? I just want to make sure there's nothing else that you think is relevant for other brands if they're looking to just be more organized on the data side of things.
[00:37:50] Speaker A: Yeah, look, eight years ago I started with this thesis of data scientists that speak advertising and that's how I was going to build my team. This sort of chasm between data and marketing people was so wide and I wanted to be the one that built that bridge.
In many ways in that eight year span, the chasm has just gotten wider. It's just gotten harder and harder to get those people speaking the same language and firing on all cylinders in the same way.
And I get the same question every time as it's like, Tim, this is interesting work that you're doing. You should just build a SaaS platform that solves this.
And there's this great, for all the nerds out there, there's this great comic called xkcd. Familiar with xkcd, Just a little goofy little cartoon and, and the guy has this one called called Standards. Two stick figures talking to each other and they're like, oh, they're like, it's so frustrating. There's 13 standards for this one thing. And the other guy says, what we should do is we should make another standard to end all this proliferation of standards. And the next thing is two people talk. And Gary goes, I can't believe there's 14 standards for the same thing.
And I'm like, look, every time you check, every year you check how many Martech platforms are there? There's 10,000, there's 15,000, there's 20,000. Guess what? There's still this huge demand for smart human beings to come in and create the sticks and glue between them, to be able to tell cross platform, do cross platform storytelling. And that problem's not going away. AI is not going to solve it. It's just going to make that proliferation worse. And so very eager to see what the agentic future holds for all of us. But yeah, like smart human beings is the solution for most of these problems.
[00:39:29] Speaker B: Yeah, and I feel like you, you talked about like platforms, but there's always going to be new platforms coming out and you have to have different integration and then even if you build new platforms, have all of them together, there's always going to be new things and we're never going to be able to like fully keep up. And I feel like, as you mentioned, that's where like human come in. Which I thought it was interesting because everyone talks about AI replacing us and then you have this different perspective. You're like, no, you need humans to be able to make this work efficiently.
That was really great, Tim. Really appreciate you coming on the show here and for anyone that wants to get in touch with you because then you have the LinkedIn, you have the website, you're active in a couple places. Where should people look for here?
[00:40:09] Speaker A: People check out latticeworkinsights.com you can reach me, Tim, at latticeworkinsights.com we run an event series called Agentic Clay.
You can go and register for events. We'll put a link in the comments.
So we're, we do events here in Los Angeles. We did five events last year, three in la, two in San Francisco. We got two in San Francisco coming up in the spring, one big one in New York, another big tent pole event in the summer agenda. Galea's just an awesome, you know, medium to bring smart folks together to support. Talk about, hey, let's match up the hype with what people are really seeing in the trenches. Like, is this really, you know, silicon is what Silicon Valley telling us actually happening?
Let's hear it. Like to bring smart folks to speak and smart folks to listen and bring them together to network afterwards. It's been a really, really big success. So yeah, Agentic LA is a big thing that we're working on to bring smart practitioners together.
[00:41:07] Speaker B: We're going to put all that in the description because I know we talked previously and there was a place you could register for it, so we'll put all that in there, the LinkedIn to website and everything like that. But yeah, thank you so much for joining us and to your listeners, if you've enjoyed this episode, make sure to leave a thumbs up, subscribe, leave a review, reach out to Tim if you need help with your data here and then until then, keep pushing and see the next one.
